Understanding the potential relationship between the socio-economic variables and contributions to OpenStreetMap
作者机构：GIScience Research GroupInstitute of GeographyHeidelberg UniversityHeidelbergGermany
出 版 物：《International Journal of Digital Earth》 (国际数字地球学报（英文）)
年 卷 期：2015年第8卷第11期
页 面：861-876页
核心收录：
学科分类：07[理学] 0708[理学-地球物理学] 0835[工学-软件工程] 0704[理学-天文学] 0811[工学-控制科学与工程] 0701[理学-数学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 070101[理学-基础数学]
基 金：The authors acknowledge the constructive comments of the anonymous reviewers and the editor-inchief which helped to improve the study.Jamal Jokar Arsanjani was funded by the Alexander von Humboldt foundation
主 题：OpenStreetMap spatial analysis collaborative mapping logistic regression analysis social science
摘 要：OpenStreetMap(OSM)has seen an exponential increase in the last few years and large volumes of geodata have been received from volunteered *** collected geodata are heterogeneous in terms of different dimensions such as spatial patterns of contributions,quality,patterns of contributing individuals,and type of *** contributors’personal information is anonymously stored by the OSM administrators,alternative methods are needed to investigate the role of contributors’characteristics on their mapping *** study is intended to explore the potential socio-economic characteristics of contributors in highly contributed areas to have better insights about the latent patterns of involved individuals in a highly dynamic state of the most active country in OSM,Germany.A logistic regression model(LRM)is applied to discover the potential correlations between dependent and independent *** findings explain that the areas with high population density,middle level of education,high income,high rate of overnight stays,high number of foreigners,and residents aged from 18 to 69 are more likely to be involved in ***,the degree of dynamism in OSM is a function of proximity to built-up ***,concluding remarks concerning the independent variables and model sensitivity are presented.
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